In the era of artificial intelligence, many applications of image generation have been proposed, e.g., image synthesis, image style conversion, generating high-quality images. However, the application of using Chinese sentences to generate images is quite rare. In addition, conventional generative adversarial networks often generate vague features on details when generating images. Adding attention mechanism to the generation of adversarial networks can match every detail in the image from every word in the sentence, such as color, body structure. Such an effect can make the system not only generate an image that roughly conforms but to generate an image that is really clear and conforms to the sentence. Therefore, this thesis uses the attention mechanism combined with the generative adversarial network to convert the Chinese sentences into high-resolution images. A model called GraGAN is proposed in this thesis. GraGAN utilizes Google's InceptionResNetV2 as an image encoder architecture and bidirectional GRU as a text encoder for training, respectively. This model has a total of three generators and three discriminators. The three generators can generate images of 64*64*3 pixels, 128*128*3 pixels, and 256*256*3 pixels. This design can use low-quality images to generate high-quality images. The three discriminators evaluate the three generators separately. The discriminator evaluation is divided into two parts, which are to discriminate whether the image is clear and whether the image meets the sentence narrative. Finally, images with high resolution and fine detail can be generated. This thesis uses Inception scores to evaluate the quality of image generation. Inception scores can determine the diversity and reality of the generated images. The experimental results show that the Inception score of GraGAN is 4.35 while training with English sentences. Other models: AttnGan is 4.33 and StackGanV2 is 4.08. While training with Chinese sentence, the Inception score of GraGAN is 4.31 and AttnGan is 4.26. Obviously, the Inception Scores of the proposed model GraGAN are higher than that of AttnGAN after adjusting the Text encoder and Image encoder. The score of the Chinese sentence training is slightly lower than that of English sentences training since a Chinese word is very different from an English word. Each English word has its own meaning and can be recognized as a token however, in Chinese, it may need to have two or three words to compose a meaningful term. Moreover, StackGanV2 is used to generate high-resolution images, but lacks of the details, so the score is significantly lower than this paper. The contribution of this thesis is to convert the dataset into Chinese sentences, and through the pre-processing of the Chinese sentence and add two-way GRU pre-training, InceptionResNetV2 coding to reduce training time and computational complexity of the proposed model GraGAN. The experimental results show that the Chinese dataset can also achieve good results as English dataset in generative adversarial network.